Abstract
The study focused on the automatic segmentation of Magnetic Resonance Imaging (MRI) images of stroke patients and the therapeutic effects of Mental Imagery on motor and neurological functions after stroke. First, the traditional fuzzy c-means (FCM) algorithm was optimized, and the optimized one was defined as filter-based FCM (FBFCM). 62 stroke patients were selected as the research subjects and randomly divided into the experimental group and the control group. The control group accepted the conventional rehabilitation training, and the experimental group accepted Mental Imagery on the basis of the control group. They all had the MRI examination, and their brain MRI images were segmented by the FBFCM algorithm. The MRI images before and after treatment were analyzed to evaluate the therapeutic effects of Mental Imagery on patients with motor and nerve dysfunction after stroke. The results showed that the segmentation coefficient of the FBFCM algorithm was 0.9315 and the segmentation entropy was 0.1098, which were significantly different from those of the traditional fuzzy c-means (FCM) algorithm. ( P < 0.05 ), suggesting that the FBFCM algorithm had good segmentation effects on brain MRI images of stroke patients. After Mental Imagery, it was found that the patient’s Function Independent Measure (FIM) score was 99.04 ± 8.19, the Modified Barthel Index (MBI) score was 51.29 ± 4.35, the Fugl-Meyer (FMA) score was 61.01 ± 4.16, the neurological deficit degree in stroke (NFDS) score was 11.48 ± 2.01, the NIH Stroke Scale (NIHSS) score was 10.36 ± 1.69, and the clinical effective rate was 87.1%, all significantly different from those of the conventional rehabilitation training group ( P < 0.05 ). Additionally, the brain area activated by Mental Imagery was more extensive. In conclusion, the FBFCM algorithm demonstrates superb capabilities in segmenting MRI images of stroke patients and is worth promotion in clinic. Mental Imagery can promote the neurological rehabilitation of patients by activating relevant brain areas of patients.
Highlights
With the development of society and the aging of the population, the incidence of stroke is increasingly high
62 patients with poststroke limb dysfunction who were treated in the hospital from December 2018 to December 2019 were selected as the research subjects. e selected cases were in line with the stroke diagnostic criteria formulated at the 4th Cerebrovascular Disease Academic Conference. ey were divided into the experimental group and the control group according to the random number table method, with 31 cases in each group
After brain Magnetic Resonance Imaging (MRI) examinations on 62 subjects, their MRI images were segmented by the filter-based FCM (FBFCM) algorithm, and the image format was set to 512 ∗ 512, the blur factor was set to 2, and the maximum number of iterations was 500. e segmentation result was shown in Figure 3. e FBFCM algorithm can segment the white matter, gray matter, and organs well in the MRI images of stroke patients
Summary
With the development of society and the aging of the population, the incidence of stroke is increasingly high. It has become the first disability factor and the second leading cause of death worldwide [1, 2]. E quality of life of poststroke patients largely depends on the degree of neurological rehabilitation [5]; rehabilitation treatment is currently a hot spot in clinical treatment of stroke. With the progress of medical technology, clinical rehabilitation techniques for poststroke neurological dysfunction have been enriched. Neurological rehabilitation training for poststroke patients, such as transcranial magnetic stimulation technology, has achieved certain clinical results. The high cost due to its special and huge equipment greatly limits its promotion
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